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20202022
most citedAvengers Ensemble! Improving Transferability of Authorship Obfuscation

2 citations · 4 across the 7 of their papers we have counts for

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cs.CL2022

Style Matters! Investigating Linguistic Style in Online Communities

Osama Khalid, Padmini Srinivasan

Content has historically been the primary lens used to study language in online communities. This paper instead focuses on the linguistic style of communities. While we know that i…

cs.CL2022

Smells like Teen Spirit: An Exploration of Sensorial Style in Literary Genres

Osama Khalid, Padmini Srinivasan

It is well recognized that sensory perceptions and language have interconnections through numerous studies in psychology, neuroscience, and sensorial linguistics. Set in this rich…

cs.CL20221 cited

Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks

Jonathan Rusert, Padmini Srinivasan

Deep learning (DL) is being used extensively for text classification. However, researchers have demonstrated the vulnerability of such classifiers to adversarial attacks. Attackers…

cs.CL20221 cited

A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation

Wanyue Zhai, Jonathan Rusert, Zubair Shafiq +1

Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution. To counter authorship attribution, researchers have proposed a variety…

cs.CL2022

Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective

Osama Khalid, Jonathan Rusert, Padmini Srinivasan

Prior research has discussed and illustrated the need to consider linguistic norms at the community level when studying taboo (hateful/offensive/toxic etc.) language. However, a me…

cs.CL2022

On The Robustness of Offensive Language Classifiers

Jonathan Rusert, Zubair Shafiq, Padmini Srinivasan

Social media platforms are deploying machine learning based offensive language classification systems to combat hateful, racist, and other forms of offensive speech at scale. Howev…